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On the basic reproduction number and the topological properties of the contact network: An epidemiological study in mainly locally connected cellular automata

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  • Schimit, P.H.T.
  • Monteiro, L.H.A.

Abstract

We study the spreading of contagious diseases in a population of constant size using susceptible-infective-recovered (SIR) models described in terms of ordinary differential equations (ODEs) and probabilistic cellular automata (PCA). In the PCA model, each individual (represented by a cell in the lattice) is mainly locally connected to others. We investigate how the topological properties of the random network representing contacts among individuals influence the transient behavior and the permanent regime of the epidemiological system described by ODE and PCA. Our main conclusions are: (1) the basic reproduction number (commonly called R0) related to a disease propagation in a population cannot be uniquely determined from some features of transient behavior of the infective group; (2) R0 cannot be associated to a unique combination of clustering coefficient and average shortest path length characterizing the contact network. We discuss how these results can embarrass the specification of control strategies for combating disease propagations.

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  • Schimit, P.H.T. & Monteiro, L.H.A., 2009. "On the basic reproduction number and the topological properties of the contact network: An epidemiological study in mainly locally connected cellular automata," Ecological Modelling, Elsevier, vol. 220(7), pages 1034-1042.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:7:p:1034-1042
    DOI: 10.1016/j.ecolmodel.2009.01.014
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    References listed on IDEAS

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    1. Kleczkowski, Adam & Grenfell, Bryan T., 1999. "Mean-field-type equations for spread of epidemics: the ‘small world’ model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 274(1), pages 355-360.
    2. Monteiro, L.H.A. & Sasso, J.B. & Chaui Berlinck, J.G., 2007. "Continuous and discrete approaches to the epidemiology of viral spreading in populations taking into account the delay of incubation time," Ecological Modelling, Elsevier, vol. 201(3), pages 553-557.
    3. Zhang, Zhibin, 2007. "The outbreak pattern of SARS cases in China as revealed by a mathematical model," Ecological Modelling, Elsevier, vol. 204(3), pages 420-426.
    4. Fuentes, M.A. & Kuperman, M.N., 1999. "Cellular automata and epidemiological models with spatial dependence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 267(3), pages 471-486.
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    Cited by:

    1. Schimit, P.H.T. & Monteiro, L.H.A., 2012. "On estimating the basic reproduction number in distinct stages of a contagious disease spreading," Ecological Modelling, Elsevier, vol. 240(C), pages 156-160.
    2. Chuangxia Huang & Jie Cao & Fenghua Wen & Xiaoguang Yang, 2016. "Stability Analysis of SIR Model with Distributed Delay on Complex Networks," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-22, August.
    3. Pereira, F.M.M. & Schimit, P.H.T., 2018. "Dengue fever spreading based on probabilistic cellular automata with two lattices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 75-87.
    4. Schimit, P.H.T. & Monteiro, L.H.A., 2010. "Who should wear mask against airborne infections? Altering the contact network for controlling the spread of contagious diseases," Ecological Modelling, Elsevier, vol. 221(9), pages 1329-1332.
    5. Jianhong Chen & Hongcai Ma & Shan Yang, 2023. "SEIOR Rumor Propagation Model Considering Hesitating Mechanism and Different Rumor-Refuting Ways in Complex Networks," Mathematics, MDPI, vol. 11(2), pages 1-22, January.
    6. Ramos, A.B.M. & Schimit, P.H.T., 2019. "Disease spreading on populations structured by groups," Applied Mathematics and Computation, Elsevier, vol. 353(C), pages 265-273.
    7. Schimit, P.H.T. & Monteiro, L.H.A., 2011. "A vaccination game based on public health actions and personal decisions," Ecological Modelling, Elsevier, vol. 222(9), pages 1651-1655.
    8. Schimit, P.H.T., 2016. "Evolutionary aspects of spatial Prisoner’s Dilemma in a population modeled by continuous probabilistic cellular automata and genetic algorithm," Applied Mathematics and Computation, Elsevier, vol. 290(C), pages 178-188.

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